Expert systems are used for a wide range of applications, including medical diagnosis, fault finding, etc. Expert systems are so named because they process data relating to a number of input conditions to derive a solution that closely matches that obtained by the thought process of an expert in the relevant field who is trying to solve a given problem in that domain. When an expert system is being constructed the knowledge of an expert needs to be captured and encoded into a knowledge base. This process involves eliciting knowledge from an expert, typically by means of an extended interview process.
Known types of expert systems utilize Bayesian networks. The construction of a Bayesian network requires a model of the problem domain to be created. By establishing links between knowledge variables the model captures the way in which the state of one variable is affected by the status of its parents. Some of the probabilities for the links between a particular variable and its parents can be difficult for an expert to provide. Further, there can often be far too many probabilities to elicit in an interview of reasonable length with an expert. There is therefore a desire to reduce the amount of data that needs to be elicited from the expert, whilst maintaining the accuracy of the generated Bayesian network.